
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask


# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")


nltk.download('all')

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True


# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_movies = pd.read_csv(path + 'ottmovies.csv')
 
df_movies.head()


# profile = ProfileReport(df_movies)
# profile


def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()


data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                 8457
IMDb                 328
Rotten Tomatoes    10437
Directors            357
Cast                 648
Genres               234
Country              303
Language             437
Plotline            4958
Runtime              382
Seasons            16923
dtype: int64
**************************************************
Missing vaules %age wise :

ID                   0.000000
Title                0.000000
Year                 0.000000
Age                 49.973409
IMDb                 1.938191
Rotten Tomatoes     61.673462
Directors            2.109555
Cast                 3.829108
Genres               1.382734
Country              1.790463
Language             2.582284
Plotline            29.297406
Runtime              2.257283
Kind                 0.000000
Seasons            100.000000
Netflix              0.000000
Hulu                 0.000000
Prime Video          0.000000
Disney+              0.000000
Type                 0.000000
dtype: float64
**************************************************
Pictorial Representation :


# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
 
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
 
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
 
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
 
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)


data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :


df_movies.head()


df_movies.describe()


df_movies.corr()


# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)


# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
 
# udf_movies


# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]


df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]


df_movies_languages = df_movies.copy()


df_movies_languages.drop(df_movies_languages.loc[df_movies_languages['Language'] == "NA"].index, inplace = True)
# df_movies_languages = df_movies_languages[df_movies_languages.Language != "NA"]
# df_movies_languages['Language'] = df_movies_languages['Language'].astype(str)


df_movies_count_languages = df_movies_languages.copy()


df_movies_language = df_movies_languages.copy()


# Create languages dict where key=name and value = number of languages
 
languages = {}
 
for i in df_movies_count_languages['Language'].dropna():
    if i != "NA":
        #print(i,len(i.split(',')))
        languages[i] = len(i.split(','))
    else:
        languages[i] = 0
    
# Add this information to our dataframe as a new column
 
df_movies_count_languages['Number of Languages'] = df_movies_count_languages['Language'].map(languages).astype(int)


df_movies_mixed_languages = df_movies_count_languages.copy()


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Netflix'] == 1]
hulu_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Hulu'] == 1]
prime_video_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Prime Video'] == 1]
disney_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Disney+'] == 1]


plt.figure(figsize = (10, 10))
corr = df_movies_count_languages.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>


df_languages_most_movies = df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = False).reset_index()
df_languages_most_movies = df_languages_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_languages['Number of Languages'] == (df_movies_count_languages['Number of Languages'].max()))
# df_languages_most_movies = df_movies_count_languages[filter]
 
# mostest_rated_movies = df_movies_count_languages.loc[df_movies_count_languages['Number of Languages'].idxmax()]
 
print('\nMovies with Highest Ever Number of Languages are : \n')
df_languages_most_movies.head(5)

Movies with Highest Ever Number of Languages are :


fig = px.bar(y = df_languages_most_movies['Title'][:15],
             x = df_languages_most_movies['Number of Languages'][:15], 
             color = df_languages_most_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Highest Number of Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_languages_least_movies = df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = True).reset_index()
df_languages_least_movies = df_languages_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_languages['Number of Languages'] == (df_movies_count_languages['Number of Languages'].min()))
# df_languages_least_movies = df_movies_count_languages[filter]

print('\nMovies with Lowest Ever Number of Languages are : \n')
df_languages_least_movies.head(5)

Movies with Lowest Ever Number of Languages are :


fig = px.bar(y = df_languages_least_movies['Title'][:15],
             x = df_languages_least_movies['Number of Languages'][:15], 
             color = df_languages_least_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Lowest Number of Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_movies_count_languages['Number of Languages'].unique().shape[0]}' unique Number of Languages s were Given, They were Like this,\n
      
      {df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = False)['Number of Languages'].unique()}\n
 
      The Highest Number of Languages Ever Any Movie Got is '{df_languages_most_movies['Title'][0]}' : '{df_languages_most_movies['Number of Languages'].max()}'\n
 
      The Lowest Number of Languages Ever Any Movie Got is '{df_languages_least_movies['Title'][0]}' : '{df_languages_least_movies['Number of Languages'].min()}'\n
      ''')

      Total '10' unique Number of Languages s were Given, They were Like this,

      
      [16 10  8  7  6  5  4  3  2  1]

 
      The Highest Number of Languages Ever Any Movie Got is 'The Jungle Book' : '16'

 
      The Lowest Number of Languages Ever Any Movie Got is 'Parasomnia' : '1'


netflix_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Netflix']==1].reset_index()
netflix_languages_most_movies = netflix_languages_most_movies.drop(['index'], axis = 1)
 
netflix_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Netflix']==1].reset_index()
netflix_languages_least_movies = netflix_languages_least_movies.drop(['index'], axis = 1)
 
netflix_languages_most_movies.head(5)


fig = px.bar(y = netflix_languages_most_movies['Title'][:15],
             x = netflix_languages_most_movies['Number of Languages'][:15], 
             color = netflix_languages_most_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Highest Number of Languages : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = netflix_languages_least_movies['Title'][:15],
             x = netflix_languages_least_movies['Number of Languages'][:15], 
             color = netflix_languages_least_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Lowest Number of Languages : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


hulu_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Hulu']==1].reset_index()
hulu_languages_most_movies = hulu_languages_most_movies.drop(['index'], axis = 1)
 
hulu_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Hulu']==1].reset_index()
hulu_languages_least_movies = hulu_languages_least_movies.drop(['index'], axis = 1)
 
hulu_languages_most_movies.head(5)


fig = px.bar(y = hulu_languages_most_movies['Title'][:15],
             x = hulu_languages_most_movies['Number of Languages'][:15], 
             color = hulu_languages_most_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Highest Number of Languages : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = hulu_languages_least_movies['Title'][:15],
             x = hulu_languages_least_movies['Number of Languages'][:15], 
             color = hulu_languages_least_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Lowest Number of Languages : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


prime_video_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Prime Video']==1].reset_index()
prime_video_languages_most_movies = prime_video_languages_most_movies.drop(['index'], axis = 1)
 
prime_video_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Prime Video']==1].reset_index()
prime_video_languages_least_movies = prime_video_languages_least_movies.drop(['index'], axis = 1)
 
prime_video_languages_most_movies.head(5)


fig = px.bar(y = prime_video_languages_most_movies['Title'][:15],
             x = prime_video_languages_most_movies['Number of Languages'][:15], 
             color = prime_video_languages_most_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Highest Number of Languages : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = prime_video_languages_least_movies['Title'][:15],
             x = prime_video_languages_least_movies['Number of Languages'][:15], 
             color = prime_video_languages_least_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Lowest Number of Languages : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


disney_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Disney+']==1].reset_index()
disney_languages_most_movies = disney_languages_most_movies.drop(['index'], axis = 1)
 
disney_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Disney+']==1].reset_index()
disney_languages_least_movies = disney_languages_least_movies.drop(['index'], axis = 1)
 
disney_languages_most_movies.head(5)


fig = px.bar(y = disney_languages_most_movies['Title'][:15],
             x = disney_languages_most_movies['Number of Languages'][:15], 
             color = disney_languages_most_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Highest Number of Languages : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = disney_languages_least_movies['Title'][:15],
             x = disney_languages_least_movies['Number of Languages'][:15], 
             color = disney_languages_least_movies['Number of Languages'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
             title  = 'Movies with Lowest Number of Languages : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      The Movie with Highest Number of Languages Ever Got is '{df_languages_most_movies['Title'][0]}' : '{df_languages_most_movies['Number of Languages'].max()}'\n
      The Movie with Lowest Number of Languages Ever Got is '{df_languages_least_movies['Title'][0]}' : '{df_languages_least_movies['Number of Languages'].min()}'\n
      
      The Movie with Highest Number of Languages on 'Netflix' is '{netflix_languages_most_movies['Title'][0]}' : '{netflix_languages_most_movies['Number of Languages'].max()}'\n
      The Movie with Lowest Number of Languages on 'Netflix' is '{netflix_languages_least_movies['Title'][0]}' : '{netflix_languages_least_movies['Number of Languages'].min()}'\n
      
      The Movie with Highest Number of Languages on 'Hulu' is '{hulu_languages_most_movies['Title'][0]}' : '{hulu_languages_most_movies['Number of Languages'].max()}'\n
      The Movie with Lowest Number of Languages on 'Hulu' is '{hulu_languages_least_movies['Title'][0]}' : '{hulu_languages_least_movies['Number of Languages'].min()}'\n
      
      The Movie with Highest Number of Languages on 'Prime Video' is '{prime_video_languages_most_movies['Title'][0]}' : '{prime_video_languages_most_movies['Number of Languages'].max()}'\n
      The Movie with Lowest Number of Languages on 'Prime Video' is '{prime_video_languages_least_movies['Title'][0]}' : '{prime_video_languages_least_movies['Number of Languages'].min()}'\n
      
      The Movie with Highest Number of Languages on 'Disney+' is '{disney_languages_most_movies['Title'][0]}' : '{disney_languages_most_movies['Number of Languages'].max()}'\n
      The Movie with Lowest Number of Languages on 'Disney+' is '{disney_languages_least_movies['Title'][0]}' : '{disney_languages_least_movies['Number of Languages'].min()}'\n 
      ''')

      The Movie with Highest Number of Languages Ever Got is 'The Jungle Book' : '16'

      The Movie with Lowest Number of Languages Ever Got is 'Parasomnia' : '1'

      
      The Movie with Highest Number of Languages on 'Netflix' is '2012' : '10'

      The Movie with Lowest Number of Languages on 'Netflix' is 'Iris' : '1'

      
      The Movie with Highest Number of Languages on 'Hulu' is 'Free Willy 2: The Adventure Home' : '8'

      The Movie with Lowest Number of Languages on 'Hulu' is 'The Impostor' : '1'

      
      The Movie with Highest Number of Languages on 'Prime Video' is 'Downsizing' : '7'

      The Movie with Lowest Number of Languages on 'Prime Video' is 'Parasomnia' : '1'

      
      The Movie with Highest Number of Languages on 'Disney+' is 'The Jungle Book' : '16'

      The Movie with Lowest Number of Languages on 'Disney+' is 'The Swap' : '1'


print(f'''
      Accross All Platforms the Average Number of Languages is '{round(df_movies_count_languages['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Netflix' is '{round(netflix_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Hulu' is '{round(hulu_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Prime Video' is '{round(prime_video_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
      The Average Number of Languages on 'Disney+' is '{round(disney_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Number of Languages is '1.28'

      The Average Number of Languages on 'Netflix' is '1.39'

      The Average Number of Languages on 'Hulu' is '1.47'

      The Average Number of Languages on 'Prime Video' is '1.24'

      The Average Number of Languages on 'Disney+' is '1.33'


print(f'''
      Accross All Platforms Total Count of Language is '{df_movies_count_languages['Number of Languages'].max()}'\n
      Total Count of Language on 'Netflix' is '{netflix_languages_movies['Number of Languages'].max()}'\n
      Total Count of Language on 'Hulu' is '{hulu_languages_movies['Number of Languages'].max()}'\n
      Total Count of Language on 'Prime Video' is '{prime_video_languages_movies['Number of Languages'].max()}'\n
      Total Count of Language on 'Disney+' is '{disney_languages_movies['Number of Languages'].max()}'\n 
      ''')

      Accross All Platforms Total Count of Language is '16'

      Total Count of Language on 'Netflix' is '10'

      Total Count of Language on 'Hulu' is '8'

      Total Count of Language on 'Prime Video' is '7'

      Total Count of Language on 'Disney+' is '16'


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_languages['Number of Languages'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_languages['Number of Languages'], ax = ax[1])
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Languages s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_languages_movies['Number of Languages'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_languages_movies['Number of Languages'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_languages_movies['Number of Languages'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_languages_movies['Number of Languages'], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


df_lan = df_movies_language['Language'].str.split(',').apply(pd.Series).stack()
del df_movies_language['Language']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Language'
df_movies_language = df_movies_language.join(df_lan)
df_movies_language.drop_duplicates(inplace = True)


df_movies_language.head(5)


language_count = df_movies_language.groupby('Language')['Title'].count()
language_movies = df_movies_language.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
language_data_movies = pd.concat([language_count, language_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
language_data_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = False)


# Language with Movies Counts - All Platforms Combined
language_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]


fig = px.bar(x = language_data_movies['Language'][:50],
             y = language_data_movies['Movies Count'][:50], 
             color = language_data_movies['Movies Count'][:50],
             color_continuous_scale = 'Teal_r', 
             labels = { 'x' : 'Language', 'y' : 'Movies Count'},
             title  = 'Major Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_language_high_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_language_high_movies = df_language_high_movies.drop(['index'], axis = 1)
# filter = (language_data_movies['Movies Count'] == (language_data_movies['Movies Count'].max()))
# df_language_high_movies = language_data_movies[filter]
 
# highest_rated_movies = language_data_movies.loc[language_data_movies['Movies Count'].idxmax()]
 
print('\nLanguage with Highest Ever Movies Count are : All Platforms Combined\n')
df_language_high_movies.head(5)

Language with Highest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_language_high_movies['Language'][:15],
             x = df_language_high_movies['Movies Count'][:15], 
             color = df_language_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Highest Movies : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_language_low_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_language_low_movies = df_language_low_movies.drop(['index'], axis = 1)
# filter = (language_data_movies['Movies Count'] == (language_data_movies['Movies Count'].min()))
# df_language_low_movies = language_data_movies[filter]

print('\nLanguage with Lowest Ever Movies Count are : All Platforms Combined\n')
df_language_low_movies.head(5)

Language with Lowest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_language_low_movies['Language'][:15],
             x = df_language_low_movies['Movies Count'][:15], 
             color = df_language_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Lowest Movies Count : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{language_data_movies['Language'].unique().shape[0]}' unique Language Count s were Given, They were Like this,\n
      
      {language_data_movies.sort_values(by = 'Movies Count', ascending = False)['Language'].unique()[:5]}\n
 
      The Highest Ever Movies Count Ever Any Movie Got is '{df_language_high_movies['Language'][0]}' : '{df_language_high_movies['Movies Count'].max()}'\n
 
      The Lowest Ever Movies Count Ever Any Movie Got is '{df_language_low_movies['Language'][0]}' : '{df_language_low_movies['Movies Count'].min()}'\n
      ''')

      Total '183' unique Language Count s were Given, They were Like this,

      
      ['English' 'Spanish' 'French' 'Hindi' 'German']

 
      The Highest Ever Movies Count Ever Any Movie Got is 'English' : '13524'

 
      The Lowest Ever Movies Count Ever Any Movie Got is 'Lao' : '1'


fig = px.pie(language_data_movies[:10], names = 'Language', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Language')
fig.show()


# netflix_language_movies = language_data_movies[language_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_language_movies = netflix_language_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_language_high_movies = df_language_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_high_movies = netflix_language_high_movies.drop(['index'], axis = 1)
 
netflix_language_low_movies = df_language_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_language_low_movies = netflix_language_low_movies.drop(['index'], axis = 1)
 
netflix_language_high_movies.head(5)


fig = px.bar(x = netflix_language_high_movies['Language'][:15],
             y = netflix_language_high_movies['Netflix'][:15], 
             color = netflix_language_high_movies['Netflix'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Highest Movies : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# hulu_language_movies = language_data_movies[language_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_language_movies = hulu_language_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_language_high_movies = df_language_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_high_movies = hulu_language_high_movies.drop(['index'], axis = 1)
 
hulu_language_low_movies = df_language_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_language_low_movies = hulu_language_low_movies.drop(['index'], axis = 1)
 
hulu_language_high_movies.head(5)


fig = px.bar(x = hulu_language_high_movies['Language'][:15],
             y = hulu_language_high_movies['Hulu'][:15], 
             color = hulu_language_high_movies['Hulu'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Highest Movies : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# prime_video_language_movies = language_data_movies[language_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_language_movies = prime_video_language_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_language_high_movies = df_language_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_high_movies = prime_video_language_high_movies.drop(['index'], axis = 1)
 
prime_video_language_low_movies = df_language_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_language_low_movies = prime_video_language_low_movies.drop(['index'], axis = 1)
 
prime_video_language_high_movies.head(5)


fig = px.bar(x = prime_video_language_high_movies['Language'][:15],
             y = prime_video_language_high_movies['Prime Video'][:15], 
             color = prime_video_language_high_movies['Prime Video'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Highest Movies : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# disney_language_movies = language_data_movies[language_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_language_movies = disney_language_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_language_high_movies = df_language_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_high_movies = disney_language_high_movies.drop(['index'], axis = 1)
 
disney_language_low_movies = df_language_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_language_low_movies = disney_language_low_movies.drop(['index'], axis = 1)
 
disney_language_high_movies.head(5)


fig = px.bar(x = disney_language_high_movies['Language'][:15],
             y = disney_language_high_movies['Disney+'][:15], 
             color = disney_language_high_movies['Disney+'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Language', 'x' : 'Movies Count'},
             title  = 'Language with Highest Movies : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(language_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(language_data_movies['Movies Count'], ax = ax[1])
plt.show()


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_language_movies = language_data_movies[language_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_movies = netflix_language_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_language_movies = language_data_movies[language_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_movies = hulu_language_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_language_movies = language_data_movies[language_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_movies = prime_video_language_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_language_movies = language_data_movies[language_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_movies = disney_language_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(disney_language_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)  
sns.histplot(prime_video_language_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_language_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_language_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()


print(f'''
      The Language with Highest Movies Count Ever Got is '{df_language_high_movies['Language'][0]}' : '{df_language_high_movies['Movies Count'].max()}'\n
      The Language with Lowest Movies Count Ever Got is '{df_language_low_movies['Language'][0]}' : '{df_language_low_movies['Movies Count'].min()}'\n
      
      The Language with Highest Movies Count on 'Netflix' is '{netflix_language_high_movies['Language'][0]}' : '{netflix_language_high_movies['Netflix'].max()}'\n
      The Language with Lowest Movies Count on 'Netflix' is '{netflix_language_low_movies['Language'][0]}' : '{netflix_language_low_movies['Netflix'].min()}'\n
      
      The Language with Highest Movies Count on 'Hulu' is '{hulu_language_high_movies['Language'][0]}' : '{hulu_language_high_movies['Hulu'].max()}'\n
      The Language with Lowest Movies Count on 'Hulu' is '{hulu_language_low_movies['Language'][0]}' : '{hulu_language_low_movies['Hulu'].min()}'\n
      
      The Language with Highest Movies Count on 'Prime Video' is '{prime_video_language_high_movies['Language'][0]}' : '{prime_video_language_high_movies['Prime Video'].max()}'\n
      The Language with Lowest Movies Count on 'Prime Video' is '{prime_video_language_low_movies['Language'][0]}' : '{prime_video_language_low_movies['Prime Video'].min()}'\n
      
      The Language with Highest Movies Count on 'Disney+' is '{disney_language_high_movies['Language'][0]}' : '{disney_language_high_movies['Disney+'].max()}'\n
      The Language with Lowest Movies Count on 'Disney+' is '{disney_language_low_movies['Language'][0]}' : '{disney_language_low_movies['Disney+'].min()}'\n 
      ''')

      The Language with Highest Movies Count Ever Got is 'English' : '13524'

      The Language with Lowest Movies Count Ever Got is 'Lao' : '1'

      
      The Language with Highest Movies Count on 'Netflix' is 'English' : '2370'

      The Language with Lowest Movies Count on 'Netflix' is 'North American Indian' : '0'

      
      The Language with Highest Movies Count on 'Hulu' is 'English' : '950'

      The Language with Lowest Movies Count on 'Hulu' is 'North American Indian' : '0'

      
      The Language with Highest Movies Count on 'Prime Video' is 'English' : '10146'

      The Language with Lowest Movies Count on 'Prime Video' is 'Greenlandic' : '0'

      
      The Language with Highest Movies Count on 'Disney+' is 'English' : '556'

      The Language with Lowest Movies Count on 'Disney+' is 'Lao' : '0'


# Distribution of movies language in each platform
plt.figure(figsize = (20, 5))
plt.title('Language with Movies Count for All Platforms')
sns.violinplot(x = language_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()


# Distribution of Language Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_language_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_language_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
 
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_language_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_language_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()


print(f'''
      Accross All Platforms the Average Movies Count of Language is '{round(language_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Language on 'Netflix' is '{round(netflix_language_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Language on 'Hulu' is '{round(hulu_language_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Language on 'Prime Video' is '{round(prime_video_language_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Language on 'Disney+' is '{round(disney_language_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Language is '115.41'

      The Average Movies Count of Language on 'Netflix' is '40.8'

      The Average Movies Count of Language on 'Hulu' is '20.52'

      The Average Movies Count of Language on 'Prime Video' is '95.85'

      The Average Movies Count of Language on 'Disney+' is '12.81'


print(f'''
      Accross All Platforms Total Count of Language is '{language_data_movies['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Netflix' is '{netflix_language_movies['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Hulu' is '{hulu_language_movies['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Prime Video' is '{prime_video_language_movies['Language'].unique().shape[0]}'\n
      Total Count of Language on 'Disney+' is '{disney_language_movies['Language'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Language is '183'

      Total Count of Language on 'Netflix' is '120'

      Total Count of Language on 'Hulu' is '75'

      Total Count of Language on 'Prime Video' is '155'

      Total Count of Language on 'Disney+' is '58'


plt.figure(figsize = (20, 5))
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Language', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_l_ax1 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_l_ax2 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_l_ax3 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_l_ax4 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = netflix_language_movies['Language'][:10], x = netflix_language_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = hulu_language_movies['Language'][:10], x = hulu_language_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = prime_video_language_movies['Language'][:10], x = prime_video_language_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = disney_language_movies['Language'][:10], x = disney_language_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_language_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_language_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_language_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_language_movies['Disney+'][:10], color = 'darkblue', legend = True)                                      
                                      
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


df_movies_mixed_languages.drop(df_movies_mixed_languages.loc[df_movies_mixed_languages['Language'] == "NA"].index, inplace = True)
# df_movies_mixed_languages = df_movies_mixed_languages[df_movies_mixed_languages.Language != "NA"]
df_movies_mixed_languages.drop(df_movies_mixed_languages.loc[df_movies_mixed_languages['Number of Languages'] == 1].index, inplace = True)


df_movies_mixed_languages.head(5)


mixed_languages_count = df_movies_mixed_languages.groupby('Language')['Title'].count()
mixed_languages_movies = df_movies_mixed_languages.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_languages_data_movies = pd.concat([mixed_languages_count, mixed_languages_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Language' : 'Mixed Language'})
mixed_languages_data_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)


mixed_languages_data_movies.head(5)


# Mixed Language with Movies Counts - All Platforms Combined
mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]


df_mixed_languages_high_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_languages_high_movies = df_mixed_languages_high_movies.drop(['index'], axis = 1)
# filter = (mixed_languages_data_movies['Movies Count'] = =  (mixed_languages_data_movies['Movies Count'].max()))
# df_mixed_languages_high_movies = mixed_languages_data_movies[filter]
 
# highest_rated_movies = mixed_languages_data_movies.loc[mixed_languages_data_movies['Movies Count'].idxmax()]
 
print('\nMixed Language with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_languages_high_movies.head(5)

Mixed Language with Highest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_mixed_languages_high_movies['Mixed Language'][:15],
             x = df_mixed_languages_high_movies['Movies Count'][:15], 
             color = df_mixed_languages_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Language'},
             title  = 'Movies with Highest Number of Mixed Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_mixed_languages_low_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_languages_low_movies = df_mixed_languages_low_movies.drop(['index'], axis = 1)
# filter = (mixed_languages_data_movies['Movies Count'] = =  (mixed_languages_data_movies['Movies Count'].min()))
# df_mixed_languages_low_movies = mixed_languages_data_movies[filter]
 
print('\nMixed Language with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_languages_low_movies.head(5)

Mixed Language with Lowest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_mixed_languages_low_movies['Mixed Language'][:15],
             x = df_mixed_languages_low_movies['Movies Count'][:15], 
             color = df_mixed_languages_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Language'},
             title  = 'Movies with Lowest Number of Mixed Languages : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_movies_languages['Language'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see Movies from Total '{mixed_languages_data_movies['Mixed Language'].unique().shape[0]}' Mixed Language, They were Like this, \n
 
      {mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Language'].head(5).unique()} etc. \n
 
      The Mixed Language with Highest Movies Count have '{mixed_languages_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_languages_high_movies['Mixed Language'][0]}', &\n
      The Mixed Language with Lowest Movies Count have '{mixed_languages_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_languages_low_movies['Mixed Language'][0]}'
      ''')

      Total '16486' Titles are available on All Platforms, out of which

      You Can Choose to see Movies from Total '1091' Mixed Language, They were Like this, 

 
      ['English,Spanish' 'English,French' 'English,German' 'English,Italian'
 'Hindi,English'] etc. 

 
      The Mixed Language with Highest Movies Count have '306' Movies Available is 'English,Spanish', &

      The Mixed Language with Lowest Movies Count have '1' Movies Available is 'Italian,Hungarian,Latin'


fig = px.pie(mixed_languages_data_movies[:10], names = 'Mixed Language', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Language')
fig.show()


# netflix_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_languages_movies = netflix_mixed_languages_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_high_movies = netflix_mixed_languages_high_movies.drop(['index'], axis = 1)
 
netflix_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_languages_low_movies = netflix_mixed_languages_low_movies.drop(['index'], axis = 1)
 
netflix_mixed_languages_high_movies.head(5)


# hulu_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_languages_movies = hulu_mixed_languages_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_high_movies = hulu_mixed_languages_high_movies.drop(['index'], axis = 1)
 
hulu_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_languages_low_movies = hulu_mixed_languages_low_movies.drop(['index'], axis = 1)
 
hulu_mixed_languages_high_movies.head(5)


# prime_video_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_languages_movies = prime_video_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_high_movies = prime_video_mixed_languages_high_movies.drop(['index'], axis = 1)
 
prime_video_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_languages_low_movies = prime_video_mixed_languages_low_movies.drop(['index'], axis = 1)
 
prime_video_mixed_languages_high_movies.head(5)


# disney_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_languages_movies = disney_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_high_movies = disney_mixed_languages_high_movies.drop(['index'], axis = 1)
 
disney_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_languages_low_movies = disney_mixed_languages_low_movies.drop(['index'], axis = 1)
 
disney_mixed_languages_high_movies.head(5)


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_languages_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_languages_data_movies['Movies Count'], ax = ax[1])
plt.show()


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_movies = netflix_mixed_languages_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_movies = hulu_mixed_languages_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_movies = prime_video_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_movies = disney_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(prime_video_mixed_languages_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_languages_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_languages_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_languages_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


print(f'''
      The Mixed Language with Highest Movies Count Ever Got is '{df_mixed_languages_high_movies['Mixed Language'][0]}' : '{df_mixed_languages_high_movies['Movies Count'].max()}'\n
      The Mixed Language with Lowest Movies Count Ever Got is '{df_mixed_languages_low_movies['Mixed Language'][0]}' : '{df_mixed_languages_low_movies['Movies Count'].min()}'\n
      
      The Mixed Language with Highest Movies Count on 'Netflix' is '{netflix_mixed_languages_high_movies['Mixed Language'][0]}' : '{netflix_mixed_languages_high_movies['Netflix'].max()}'\n
      The Mixed Language with Lowest Movies Count on 'Netflix' is '{netflix_mixed_languages_low_movies['Mixed Language'][0]}' : '{netflix_mixed_languages_low_movies['Netflix'].min()}'\n
      
      The Mixed Language with Highest Movies Count on 'Hulu' is '{hulu_mixed_languages_high_movies['Mixed Language'][0]}' : '{hulu_mixed_languages_high_movies['Hulu'].max()}'\n
      The Mixed Language with Lowest Movies Count on 'Hulu' is '{hulu_mixed_languages_low_movies['Mixed Language'][0]}' : '{hulu_mixed_languages_low_movies['Hulu'].min()}'\n
      
      The Mixed Language with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_languages_high_movies['Mixed Language'][0]}' : '{prime_video_mixed_languages_high_movies['Prime Video'].max()}'\n
      The Mixed Language with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_languages_low_movies['Mixed Language'][0]}' : '{prime_video_mixed_languages_low_movies['Prime Video'].min()}'\n
      
      The Mixed Language with Highest Movies Count on 'Disney+' is '{disney_mixed_languages_high_movies['Mixed Language'][0]}' : '{disney_mixed_languages_high_movies['Disney+'].max()}'\n
      The Mixed Language with Lowest Movies Count on 'Disney+' is '{disney_mixed_languages_low_movies['Mixed Language'][0]}' : '{disney_mixed_languages_low_movies['Disney+'].min()}'\n 
      ''')

      The Mixed Language with Highest Movies Count Ever Got is 'English,Spanish' : '306'

      The Mixed Language with Lowest Movies Count Ever Got is 'Italian,Hungarian,Latin' : '1'

      
      The Mixed Language with Highest Movies Count on 'Netflix' is 'English,Spanish' : '68'

      The Mixed Language with Lowest Movies Count on 'Netflix' is 'English,Polish,Cantonese,Italian' : '0'

      
      The Mixed Language with Highest Movies Count on 'Hulu' is 'English,Spanish' : '39'

      The Mixed Language with Lowest Movies Count on 'Hulu' is 'English,Polish,Cantonese,Italian' : '0'

      
      The Mixed Language with Highest Movies Count on 'Prime Video' is 'English,Spanish' : '208'

      The Mixed Language with Lowest Movies Count on 'Prime Video' is 'Yoruba,Ibo,English' : '0'

      
      The Mixed Language with Highest Movies Count on 'Disney+' is 'English,French' : '17'

      The Mixed Language with Lowest Movies Count on 'Disney+' is 'English,Polish,Cantonese,Italian' : '0'


print(f'''
      Accross All Platforms the Average Movies Count of Mixed Language is '{round(mixed_languages_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Language on 'Netflix' is '{round(netflix_mixed_languages_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Language on 'Hulu' is '{round(hulu_mixed_languages_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Language on 'Prime Video' is '{round(prime_video_mixed_languages_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Language on 'Disney+' is '{round(disney_mixed_languages_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Mixed Language is '2.76'

      The Average Movies Count of Mixed Language on 'Netflix' is '2.06'

      The Average Movies Count of Mixed Language on 'Hulu' is '1.82'

      The Average Movies Count of Mixed Language on 'Prime Video' is '2.57'

      The Average Movies Count of Mixed Language on 'Disney+' is '1.89'


print(f'''
      Accross All Platforms Total Count of Mixed Language is '{mixed_languages_data_movies['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Netflix' is '{netflix_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Hulu' is '{hulu_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Prime Video' is '{prime_video_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
      Total Count of Mixed Language on 'Disney+' is '{disney_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Mixed Language is '1091'

      Total Count of Mixed Language on 'Netflix' is '417'

      Total Count of Mixed Language on 'Hulu' is '157'

      Total Count of Mixed Language on 'Prime Video' is '751'

      Total Count of Mixed Language on 'Disney+' is '57'


plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Language', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_l_ax1 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_ml_ax1 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_ml_ax2 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_ml_ax3 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_ml_ax4 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_languages_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_languages_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_languages_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_languages_movies['Disney+'][:50], color = 'darkblue', legend = True)                                      

# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ml_ax1 = sns.barplot(y = netflix_mixed_languages_movies['Mixed Language'][:10], x = netflix_mixed_languages_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ml_ax2 = sns.barplot(y = hulu_mixed_languages_movies['Mixed Language'][:10], x = hulu_mixed_languages_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ml_ax3 = sns.barplot(y = prime_video_mixed_languages_movies['Mixed Language'][:10], x = prime_video_mixed_languages_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ml_ax4 = sns.barplot(y = disney_mixed_languages_movies['Mixed Language'][:10], x = disney_mixed_languages_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
 
plt.show()


fig = go.Figure(go.Funnel(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Movies Count'][:10]))
fig.show()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Prime Video
0	1	Inception	2010	13+	8.8	87%	Christopher Nolan	Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ...	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	Dom Cobb is a skilled thief, the absolute best...	148.0	movie	NaN	1	0
1	2	The Matrix	1999	16+	8.7	88%	Lana Wachowski,Lilly Wachowski	Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo...	Action,Sci-Fi	United States	English	Thomas A. Anderson is a man living two lives. ...	136.0	movie	NaN	1	0
2	3	Avengers: Infinity War	2018	13+	8.4	85%	Anthony Russo,Joe Russo	Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo...	Action,Adventure,Sci-Fi	United States	English	As the Avengers and their allies have continue...	149.0	movie	NaN	1	0
3	4	Back to the Future	1985	7+	8.5	96%	Robert Zemeckis	Michael J. Fox,Christopher Lloyd,Lea Thompson,...	Adventure,Comedy,Sci-Fi	United States	English	Marty McFly, a typical American teenager of th...	116.0	movie	NaN	1	0
4	5	The Good, the Bad and the Ugly	1966	16+	8.8	97%	Sergio Leone	Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ...	Western	Italy,Spain,West Germany,United States	Italian	Blondie (The Good) (Clint Eastwood) is a profe...	161.0	movie	NaN	1	1

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Netflix	Prime Video	Service Provider
0	1	Inception	2010	13	8.8	87	Christopher Nolan	Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ...	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	Dom Cobb is a skilled thief, the absolute best...	148	movie	1	0	Netflix
1	2	The Matrix	1999	16	8.7	88	Lana Wachowski,Lilly Wachowski	Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo...	Action,Sci-Fi	United States	English	Thomas A. Anderson is a man living two lives. ...	136	movie	1	0	Netflix
2	3	Avengers: Infinity War	2018	13	8.4	85	Anthony Russo,Joe Russo	Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo...	Action,Adventure,Sci-Fi	United States	English	As the Avengers and their allies have continue...	149	movie	1	0	Netflix
3	4	Back to the Future	1985	7	8.5	96	Robert Zemeckis	Michael J. Fox,Christopher Lloyd,Lea Thompson,...	Adventure,Comedy,Sci-Fi	United States	English	Marty McFly, a typical American teenager of th...	116	movie	1	0	Netflix
4	5	The Good, the Bad and the Ugly	1966	16	8.8	97	Sergio Leone	Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ...	Western	Italy,Spain,West Germany,United States	Italian	Blondie (The Good) (Clint Eastwood) is a profe...	161	movie	1	1	Netflix

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	16923.000000	16923.000000	16923.000000	16923.000000	16923.000000	16923.000000	16923.0
mean	8462.000000	2003.211901	0.214915	0.062637	0.727235	0.033150	0.0
std	4885.393638	20.526532	0.410775	0.242315	0.445394	0.179034	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	0.0
25%	4231.500000	2001.000000	0.000000	0.000000	0.000000	0.000000	0.0
50%	8462.000000	2012.000000	0.000000	0.000000	1.000000	0.000000	0.0
75%	12692.500000	2016.000000	0.000000	0.000000	1.000000	0.000000	0.0
max	16923.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	0.0

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.217816	-0.644470	-0.129926	0.469301	0.263530	NaN
Year	-0.217816	1.000000	0.256151	0.101337	-0.255578	-0.047258	NaN
Netflix	-0.644470	0.256151	1.000000	-0.118032	-0.745141	-0.089649	NaN
Hulu	-0.129926	0.101337	-0.118032	1.000000	-0.284654	-0.039693	NaN
Prime Video	0.469301	-0.255578	-0.745141	-0.284654	1.000000	-0.289008	NaN
Disney+	0.263530	-0.047258	-0.089649	-0.039693	-0.289008	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Disney+	Service Provider	Number of Languages
0	15782	The Jungle Book	1967	7	7.4	94	Jon Favreau	Neel Sethi,Bill Murray,Ben Kingsley,Idris Elba...	Adventure,Drama,Family,Fantasy	United Kingdom,United States	...	While the First Order continues to ravage the ...	106	movie	1	Disney+	16
1	16916	Iron Man	1994	16	7.9	60	Jon Favreau	Robert Downey Jr.,Terrence Howard,Jeff Bridges...	Action,Adventure,Sci-Fi	United States,Canada	...	NA	126	movie	1	Disney+	7
2	15746	Captain America: Civil War	2016	13	7.8	90	Anthony Russo,Joe Russo	Chris Evans,Robert Downey Jr.,Scarlett Johanss...	Action,Adventure,Sci-Fi	United States	...	All looks lost for the Rebellion against the E...	147	movie	1	Disney+	6
3	15802	Miracle on 34th Street	1947	7	6.6	96	Les Mayfield	Richard Attenborough,Elizabeth Perkins,Dylan M...	Family,Fantasy	United States	...	A young boy, Pete, is found in a forest where ...	114	movie	1	Disney+	6
4	16073	The Brave Little Toaster to the Rescue	1997	0	6.4	40	Robert C. Ramirez,Patrick A. Ventura	Jessica Tuck,Chris Young,Roger Kabler,Deanna O...	Animation,Adventure,Family,Fantasy	United States	...	Freshman Jamie Bartlett is frustrated with hig...	74	movie	1	Disney+	6

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Prime Video	Service Provider	Number of Languages
0	8405	Parasomnia	2008	16	5.2	NA	William Malone	Sean Young,Patrick Kilpatrick,Dylan McKnight,C...	Horror,Thriller	United States	...	Autopsy examines how forensic examiners can he...	103	movie	1	Prime Video	1
1	10849	Road to the Open	2014	7	6.7	NA	Cole Claassen	Troy McKay,Phillip DeVona,Michelle Gunn,Judd N...	Comedy,Drama	United States	...	Having a child die is the worst thing imaginab...	90	movie	1	Prime Video	1
2	10850	Blinder	2013	NR	4.5	0	Richard Gray	Oliver Ackland,Jack Thompson,Zoe Carides,Anna ...	Drama,Romance,Sport	Australia	...	Bernard and Claire Boiko won enough money on T...	102	movie	1	Prime Video	1
3	10851	The Last Hit Man	2008	NR	5.3	NA	Christopher Warre Smets	Joe Mantegna,Elizabeth Whitmere,Romano Orzari,...	Action,Crime,Drama,Thriller	Canada	...	AND THEN THERE WAS LIGHT tells the story of Ba...	90	movie	1	Prime Video	1
4	10852	The Kung Fu Instructor	1979	16	6.4	NA	Chung Sun	Lung Ti,Yue Wong,Feng Ku,Angie Chiu,Lung-Wei W...	Action,Drama	Hong Kong	...	NA	100	movie	1	Prime Video	1

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Netflix	Service Provider	Number of Languages
0	456	2012	2009	13	5.8	39	Roland Emmerich	John Cusack,Amanda Peet,Chiwetel Ejiofor,Thand...	Action,Adventure,Sci-Fi	United States	...	Dr. Adrian Helmsley, part of a worldwide geoph...	158	movie	1	Netflix	10
1	555	American Assassin	2017	16	6.2	34	Michael Cuesta	Dylan O'Brien,Charlotte Vega,Christopher Bomfo...	Action,Thriller	United States	...	Twenty three-year-old Mitch lost his parents t...	111	movie	1	Netflix	8
2	449	Die Another Day	2002	13	6.1	56	Lee Tamahori	Pierce Brosnan,Halle Berry,Toby Stephens,Rosam...	Action,Adventure,Thriller	United Kingdom,United States,Spain,Iceland	...	Pierce Brosnan gives one last mission as James...	133	movie	1	Netflix	8
3	511	The Wandering Earth	2019	13	6	70	Frant Gwo	Jing Wu,Chuxiao Qu,Guangjie Li,Man-Tat Ng,Jin ...	Action,Sci-Fi	China	...	The sun is dying out. The earth will soon be e...	125	movie	1	Netflix	8
4	144	Babel	2006	16	7.4	69	Alejandro G. Iñárritu	Brad Pitt,Cate Blanchett,Mohamed Akhzam,Peter ...	Drama	United States,Mexico,France,Morocco,Japan	...	4 interlocking stories connected by a single g...	143	movie	1	Netflix	8

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Hulu	Prime Video	Service Provider	Number of Languages
0	3940	Free Willy 2: The Adventure Home	1995	7	5.1	50	Dwight H. Little	Jason James Richter,Francis Capra,Mary Kate Sc...	Adventure,Drama,Family	France,United States,Luxembourg	...	Willy the smart and rebellious whale and Jessi...	95	movie	1	0	Hulu	8
1	3777	Downsizing	2017	16	5.7	47	Alexander Payne	Matt Damon,Christoph Waltz,Hong Chau,Kristen W...	Drama,Fantasy,Sci-Fi	United States,Norway	...	A new world of possibilities awaits, thanks to...	135	movie	1	1	Prime Video	7
2	3523	Embrace of the Serpent	2015	NR	7.9	96	Ciro Guerra	Nilbio Torres,Antonio Bolívar,Jan Bijvoet,Brio...	Adventure,Biography,Drama	Colombia,Venezuela,Argentina	...	NA	125	movie	1	1	Prime Video	7
3	16424	The Exorcist	2016	16	8	89	William Friedkin	Ellen Burstyn,Max von Sydow,Lee J. Cobb,Kitty ...	Horror	United States	...	NA	122	movie	1	0	Hulu	7
4	3916	Terminal	2018	13	7.4	33	Steven Spielberg	Tom Hanks,Catherine Zeta-Jones,Stanley Tucci,C...	Comedy,Drama,Romance	United States	...	Victor Navorski reaches JFK airport from a pol...	128	movie	1	0	Hulu	7

	Language	Movies Count	Netflix	Hulu	Prime Video	Disney+
46	English	13524	2370	950	10146	556
156	Spanish	953	356	88	528	23
53	French	875	239	102	553	28
69	Hindi	720	368	6	410	7
58	German	526	118	53	361	16
77	Italian	491	87	32	378	11
102	Mandarin	371	91	21	276	9
78	Japanese	340	101	49	199	5
139	Russian	238	60	24	159	6
11	Arabic	196	91	12	93	5

	Language	Movies Count	Netflix	Hulu	Prime Video
0	Lao	1	0	0	1
1	Belarusian	1	0	0	1
2	Bemba	1	0	1	0
3	Hakka	1	0	0	1
4	Berber languages	1	1	0	0

	Mixed Language	Movies Count	Netflix	Hulu	Prime Video	Disney+
510	English,Spanish	306	68	39	208	13
181	English,French	201	50	28	122	17
244	English,German	87	16	2	66	8
334	English,Italian	78	11	6	58	3
739	Hindi,English	64	39	1	31	0

	Mixed Language	Movies Count	Netflix	Prime Video
0	Italian,Hungarian,Latin	1	0	1
1	English,French,Tibetan,Mandarin,Russian,Hindi,...	1	1	0
2	English,French,Turkish,German,Greek,Italian	1	0	1
3	English,French,Ukrainian	1	1	0
4	English,Gallegan	1	0	1

	Mixed Language	Movies Count	Netflix	Hulu	Prime Video	Disney+
0	English,Spanish	306	68	39	208	13
1	English,French	201	50	28	122	17
2	English,Mandarin	36	12	8	20	3
3	English,Italian	78	11	6	58	3
4	French,English	45	7	6	32	0